Behnam Neyshabur

Publication Activity (10 Years)

Years Active: 2013-2023
Publications (10 Years): 84

Top Topics

Matrix Factorization

Top Venues

Publications

Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR (2023)
Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, Behnam Neyshabur
REPAIR: REnormalizing Permuted Activations for Interpolation Repair. ICLR (2023)
Harsh Mehta, Ankit Gupta, Ashok Cutkosky, Behnam Neyshabur
Long Range Language Modeling via Gated State Spaces. ICLR (2023)
Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi
Leveraging unlabeled data to predict out-of-distribution performance. ICLR (2022)
Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Colin Cherry, Behnam Neyshabur, Orhan Firat
Data Scaling Laws in NMT: The Effect of Noise and Architecture. ICML (2022)
Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron C. Courville, Behnam Neyshabur, Hanie Sedghi
Teaching Algorithmic Reasoning via In-context Learning. CoRR (2022)
Lukas Timpl, Rahim Entezari, Hanie Sedghi, Behnam Neyshabur, Olga Saukh
Understanding the effect of sparsity on neural networks robustness. CoRR (2022)
DeLesley Hutchins, Imanol Schlag, Yuhuai Wu, Ethan Dyer, Behnam Neyshabur
Block-Recurrent Transformers. CoRR (2022)
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay V. Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, Behnam Neyshabur
Exploring Length Generalization in Large Language Models. NeurIPS (2022)
Amr Khalifa, Michael C. Mozer, Hanie Sedghi, Behnam Neyshabur, Ibrahim Alabdulmohsin
Layer-Stack Temperature Scaling. CoRR (2022)
Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi
Exploring the Limits of Large Scale Pre-training. ICLR (2022)
Harsh Mehta, Ankit Gupta, Ashok Cutkosky, Behnam Neyshabur
Long Range Language Modeling via Gated State Spaces. CoRR (2022)
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay V. Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra
Solving Quantitative Reasoning Problems with Language Models. NeurIPS (2022)
Ibrahim Alabdulmohsin, Behnam Neyshabur, Xiaohua Zhai
Revisiting Neural Scaling Laws in Language and Vision. CoRR (2022)
Ibrahim M. Alabdulmohsin, Behnam Neyshabur, Xiaohua Zhai
Revisiting Neural Scaling Laws in Language and Vision. NeurIPS (2022)
Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, Behnam Neyshabur
REPAIR: REnormalizing Permuted Activations for Interpolation Repair. CoRR (2022)
DeLesley Hutchins, Imanol Schlag, Yuhuai Wu, Ethan Dyer, Behnam Neyshabur
Block-Recurrent Transformers. NeurIPS (2022)
Tolga Ergen, Behnam Neyshabur, Harsh Mehta
Convexifying Transformers: Improving optimization and understanding of transformer networks. CoRR (2022)
Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George Edward Dahl, Zachary Nado, Orhan Firat
A Loss Curvature Perspective on Training Instabilities of Deep Learning Models. ICLR (2022)
Anders Johan Andreassen, Yasaman Bahri, Behnam Neyshabur, Rebecca Roelofs
The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning. Trans. Mach. Learn. Res. 2022 (2022)
Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Maxim Krikun, Colin Cherry, Behnam Neyshabur, Orhan Firat
Data Scaling Laws in NMT: The Effect of Noise and Architecture. CoRR (2022)
Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay V. Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra
Solving Quantitative Reasoning Problems with Language Models. CoRR (2022)
Rahim Entezari, Hanie Sedghi, Olga Saukh, Behnam Neyshabur
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks. ICLR (2022)
Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton, Behnam Neyshabur, Hanie Sedghi
Leveraging Unlabeled Data to Predict Out-of-Distribution Performance. CoRR (2022)
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay V. Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, Behnam Neyshabur
Exploring Length Generalization in Large Language Models. CoRR (2022)
Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers. ICLR (2021)
Harsh Mehta, Ashok Cutkosky, Behnam Neyshabur
Extreme Memorization via Scale of Initialization. ICLR (2021)
Anders Andreassen, Yasaman Bahri, Behnam Neyshabur, Rebecca Roelofs
The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning. CoRR (2021)
Robert J. N. Baldock, Hartmut Maennel, Behnam Neyshabur
Deep Learning Through the Lens of Example Difficulty. NeurIPS (2021)
Xiaoxia Wu, Ethan Dyer, Behnam Neyshabur
When Do Curricula Work? ICLR (2021)
Robert J. N. Baldock, Hartmut Maennel, Behnam Neyshabur
Deep Learning Through the Lens of Example Difficulty. CoRR (2021)
Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi
Exploring the Limits of Large Scale Pre-training. CoRR (2021)
Rahim Entezari, Hanie Sedghi, Olga Saukh, Behnam Neyshabur
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks. CoRR (2021)
Vaishnavh Nagarajan, Anders Andreassen, Behnam Neyshabur
Understanding the failure modes of out-of-distribution generalization. ICLR (2021)
Anna Golubeva, Guy Gur-Ari, Behnam Neyshabur
Are wider nets better given the same number of parameters? ICLR (2021)
Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George E. Dahl, Zachary Nado, Orhan Firat
A Loss Curvature Perspective on Training Instability in Deep Learning. CoRR (2021)
Pierre Foret, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur
Sharpness-aware Minimization for Efficiently Improving Generalization. ICLR (2021)
Pierre Foret, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur
Sharpness-Aware Minimization for Efficiently Improving Generalization. CoRR (2020)
Behnam Neyshabur
Towards Learning Convolutions from Scratch. CoRR (2020)
Yiding Jiang, Parth Natekar, Manik Sharma, Sumukh K. Aithal, Dhruva Kashyap, Natarajan Subramanyam, Carlos Lassance, Daniel M. Roy, Gintare Karolina Dziugaite, Suriya Gunasekar, Isabelle Guyon, Pierre Foret, Scott Yak, Hossein Mobahi, Behnam Neyshabur, Samy Bengio
Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning. NeurIPS (Competition and Demos) (2020)
Anna Golubeva, Behnam Neyshabur, Guy Gur-Ari
Are wider nets better given the same number of parameters? CoRR (2020)
Behnam Neyshabur
Towards Learning Convolutions from Scratch. NeurIPS (2020)
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio
Fantastic Generalization Measures and Where to Find Them. ICLR (2020)
Yiding Jiang, Pierre Foret, Scott Yak, Daniel M. Roy, Hossein Mobahi, Gintare Karolina Dziugaite, Samy Bengio, Suriya Gunasekar, Isabelle Guyon, Behnam Neyshabur
NeurIPS 2020 Competition: Predicting Generalization in Deep Learning. CoRR (2020)
Xiaoxia Wu, Ethan Dyer, Behnam Neyshabur
When Do Curricula Work? CoRR (2020)
Vaishnavh Nagarajan, Anders Andreassen, Behnam Neyshabur
Understanding the Failure Modes of Out-of-Distribution Generalization. CoRR (2020)
Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi
The intriguing role of module criticality in the generalization of deep networks. ICLR (2020)
Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang
What is being transferred in transfer learning? CoRR (2020)
Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang
What is being transferred in transfer learning? NeurIPS (2020)
Harsh Mehta, Ashok Cutkosky, Behnam Neyshabur
Extreme Memorization via Scale of Initialization. CoRR (2020)
Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur
Observational Overfitting in Reinforcement Learning. ICLR (2020)
Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi
The Deep Bootstrap: Good Online Learners are Good Offline Generalizers. CoRR (2020)
Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio
Fantastic Generalization Measures and Where to Find Them. CoRR (2019)
Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur
Observational Overfitting in Reinforcement Learning. CoRR (2019)
Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi
The intriguing role of module criticality in the generalization of deep networks. CoRR (2019)
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro
The role of over-parametrization in generalization of neural networks. ICLR (Poster) (2019)
Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang
Stronger Generalization Bounds for Deep Nets via a Compression Approach. ICML (2018)
Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang
Stronger generalization bounds for deep nets via a compression approach. CoRR (2018)
Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. ICLR (Poster) (2018)
Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro
Implicit Regularization in Matrix Factorization. ITA (2018)
Somaye Hashemifar, Behnam Neyshabur, Aly A. Khan, Jinbo Xu
Predicting protein-protein interactions through sequence-based deep learning. Bioinform. 34 (17) (2018)
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro
Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks. CoRR (2018)
Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro
Implicit Regularization in Matrix Factorization. CoRR (2017)
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. CoRR (2017)
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nati Srebro
Exploring Generalization in Deep Learning. NIPS (2017)
Behnam Neyshabur
Implicit Regularization in Deep Learning. CoRR (2017)
Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro
Implicit Regularization in Matrix Factorization. NIPS (2017)
Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
Geometry of Optimization and Implicit Regularization in Deep Learning. CoRR (2017)
Behnam Neyshabur, Srinadh Bhojanapalli, Ayan Chakrabarti
Stabilizing GAN Training with Multiple Random Projections. CoRR (2017)
Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire
Corralling a Band of Bandit Algorithms. COLT (2017)
Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro
Exploring Generalization in Deep Learning. CoRR (2017)
Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro
Global Optimality of Local Search for Low Rank Matrix Recovery. CoRR (2016)
Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nathan Srebro
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. CoRR (2016)
Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro
Global Optimality of Local Search for Low Rank Matrix Recovery. NIPS (2016)
Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nati Srebro
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. NIPS (2016)
Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire
Corralling a Band of Bandit Algorithms. CoRR (2016)
Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
Data-Dependent Path Normalization in Neural Networks. ICLR (Poster) (2016)
Behnam Neyshabur, Ryota Tomioka, Nathan Srebro
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning. ICLR (Workshop) (2015)
Somaye Hashemifar, Behnam Neyshabur, Jinbo Xu
Joint inference of tissue-specific networks with a scale free topology. BIBM (2015)
Behnam Neyshabur, Nathan Srebro
On Symmetric and Asymmetric LSHs for Inner Product Search. ICML (2015)
Behnam Neyshabur, Ryota Tomioka, Nathan Srebro
Norm-Based Capacity Control in Neural Networks. CoRR (2015)
Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro
Path-SGD: Path-Normalized Optimization in Deep Neural Networks. NIPS (2015)
Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro
Path-SGD: Path-Normalized Optimization in Deep Neural Networks. CoRR (2015)
Behnam Neyshabur, Ryota Tomioka, Nathan Srebro
Norm-Based Capacity Control in Neural Networks. COLT (2015)
Behnam Neyshabur, Nathan Srebro
On Symmetric and Asymmetric LSHs for Inner Product Search. CoRR (2014)
Behnam Neyshabur, Yury Makarychev, Nathan Srebro
Clustering, Hamming Embedding, Generalized LSH and the Max Norm. ALT (2014)
Behnam Neyshabur, Yury Makarychev, Nathan Srebro
Clustering, Hamming Embedding, Generalized LSH and the Max Norm. CoRR (2014)
Behnam Neyshabur, Nati Srebro, Ruslan Salakhutdinov, Yury Makarychev, Payman Yadollahpour
The Power of Asymmetry in Binary Hashing. NIPS (2013)
Behnam Neyshabur, Ahmadreza Khadem, Somaye Hashemifar, Seyed Shahriar Arab
NETAL: a new graph-based method for global alignment of protein-protein interaction networks. Bioinform. 29 (13) (2013)
Behnam Neyshabur, Payman Yadollahpour, Yury Makarychev, Ruslan Salakhutdinov, Nathan Srebro
The Power of Asymmetry in Binary Hashing. CoRR (2013)
Behnam Neyshabur, Rina Panigrahy
Sparse Matrix Factorization. CoRR (2013)